论文标题
香草风格的深度学习模型的基本合奏改善了CT图像的肝分割
Basic Ensembles of Vanilla-Style Deep Learning Models Improve Liver Segmentation From CT Images
论文作者
论文摘要
从3D计算机断层扫描（CT）图像对肝脏进行分割是医学图像分析中最常执行的操作之一。在过去的十年中，深度学习模型（DMS）比以前的肝脏分割方法提供了显着改善。 DMS的成功通常归功于用户在深度学习方面的专业知识以及复杂的培训程序。对定制专业知识的需求限制了涉及DMS的经验研究的可重复性。当今的共识是，DMS的合奏比单个组件DMS更好。在这项研究中，我们着手探索公开可用的合奏的潜力，“香草风格”的DM分段我们的合奏是由四个现成的DMS创建的：U-NET，DeepMedic，V-Net和密集的V-Networks。为了防止进一步的过度拟合并保持整体模型的简单，我们使用基本的不可训练的合奏组合：多数投票，平均值，产品和最小/最大。我们使用两个公开数据集（混乱和3Dircadb1）的结果表明，在四个广泛使用的指标上，集合比单个细分机要好得多。
Segmentation of the liver from 3D computer tomography (CT) images is one of the most frequently performed operations in medical image analysis. In the past decade, Deep Learning Models (DMs) have offered significant improvements over previous methods for liver segmentation. The success of DMs is usually owed to the user's expertise in deep learning as well as to intricate training procedures. The need for bespoke expertise limits the reproducibility of empirical studies involving DMs. Today's consensus is that an ensemble of DMs works better than the individual component DMs. In this study we set off to explore the potential of ensembles of publicly available, `vanilla-style' DM segmenters Our ensembles were created from four off-the-shelf DMs: U-Net, Deepmedic, V-Net, and Dense V-Networks. To prevent further overfitting and to keep the overall model simple, we use basic non-trainable ensemble combiners: majority vote, average, product and min/max. Our results with two publicly available data sets (CHAOS and 3Dircadb1) demonstrate that ensembles are significantly better than the individual segmenters on four widely used metrics.
